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A novel MADM model integrating hybrid information for evaluating the development prospects of urban new energy vehicles
As an effective approach to mitigating urban environmental issues, New Energy Vehicles (NEVs) have become a focal point of research regarding their current development status and future prospects in China. Addressing the significant disparities in the development of the NEVs industry across different cities, this study focuses on ten typical Chinese cities and develops a novel multi-attribute decision-making (MADM) framework to evaluate the prospects of NEVs promotion in these cities. The study first establishes a comprehensive indicator system that covers key dimensions such as economy, policy support, infrastructure, technological innovation, and environment, encompassing five different types of evaluation information. This system incorporates five different types of evaluation information: exact numbers, interval numbers, triangular fuzzy numbers, hesitant fuzzy numbers, and probabilistic linguistic term sets (PLTS), enhancing the framework’s ability to handle diverse data types. Subsequently, the improved entropy (IEntropy) weight method is employed to determine the objective weights of the evaluation indicators. These objective weights are then integrated with the Vlsekriterijumska Optimizacija I Kompromisno Resenje (VIKOR) method, facilitating a structured group decision-making approach that synthesizes hybrid evaluation information. Based on modular thinking, hybrid evaluation information is synthesized to evaluate and rank the NEVs development prospects of each city. Sensitivity analysis and comparative analysis further demonstrate the robustness and reliability of the proposed MADM framework. The ranking results indicate that Shanghai and Guangzhou lead in NEVs promotion, while cities like Harbin and Zhengzhou lag behind. Based on these findings, the study proposes targeted policy recommendations to promote the sustainable development of the NEVs industry in major Chinese cities.
Heterogeneous and multiple conformational transition pathways between pseudoknots of the SARS-CoV-2 frameshift element
Frameshifting is an essential mechanism employed by many viruses including coronaviruses to produce viral proteins from a compact RNA genome. It is facilitated by specific RNA folds in the frameshift element (FSE), which has emerged as an important therapeutic target. For SARS-CoV-2, a specific 3-stem pseudoknot has been identified to stimulate frameshifting. However, prior studies and our RNA-As-Graphs analysis coupled to chemical reactivity experiments revealed other folds, including a different pseudoknot. Although structural plasticity has been proposed to play a key role in frameshifting, paths between different FSE RNA folds have not been yet identified. Here, we capture atomic-level transition pathways between two key FSE pseudoknots by transition path sampling coupled to Markov State Modeling and our BOLAS free energy method. We reveal multiple transition paths within a heterogeneous, multihub conformational landscape. A shared folding mechanism involves RNA stem unpairing followed by a 5 ′ -chain end release. Significantly, this pseudoknot transition critically tunes the tension through the RNA spacer region and places the viral RNA in the narrow ribosomal channel. Our work further explains the role of the alternative pseudoknot in ribosomal pausing and clarifies why the experimentally captured pseudoknot is preferred for frameshifting. Our capturing of this large-scale transition of RNA secondary and tertiary structure highlights the complex pathways of biomolecules and the inherent multifarious aspects that viruses developed to ensure virulence and survival. This enhanced understanding of viral frameshifting also provides insights to target key transitions for therapeutic applications. Our methods are generally applicable to other large-scale biomolecular transitions.
Use of evidence-based health professions education: Attitudes and practices of academic leaders
Purpose The objective of this study was to explore the attitudes, practices, supports, and barriers of academic leaders regarding the use of Evidence-Based Health Professional Education (EBHPE). Methods A cross-sectional survey was conducted on 79 faculty members in leadership positions, from four different undergraduate colleges at Qassim University. A pre-validated questionnaire was distributed electronically. The e-questionnaire was comprised of 38 questions regarding participants’ demographics, attitudes, practices, and perceived barriers/supports towards EBHPE. Data was analyzed using SPSS. Descriptive statistics were calculated for demographic characteristics and responses to survey items. The frequency distribution of the subjects was analyzed, and the median and means were calculated. Kruskall-Wallis and Mann-Whitney U tests were used for comparison of the means between the demographic groups. Spearman correlation was utilized to determine any relationships between the questionnaire’s three sections. Results Of the 79 participants, 24 were Department Heads and 48 were female. The mean±SD values for Attitude, Practices and Support/Barriers were found to be 3.47±0.40, 2.72±0.84 and 3.34±0.43, respectively. The mean value of Attitude Score was > 3.41, therefore, it was interpreted that respondents had positive attitude towards EBHPE. Conversely, the values for Practices and Support/Barriers were interpreted as neutral (range = 2.61–3.40), neither good nor bad. The correlation coefficients between ’attitudes’ and ’supports and barriers’ (0.388) and ‘practices’ and ‘supports and barriers’ (0.388) indicated the weak association between these factors. The Cronbach’s α value for questionnaire was found to be 0.887 indicating its reliability. Conclusions This study concludes that the respondents had positive attitude towards EBHPE. However, this positive attitude was not reflected in their practices. Healthcare professionals should be committed to educational excellence, and endeavour to rely on evidence-based literature regarding the planning and review of learning, teaching, and assessment strategies.
Evidence that ARK2N is not a core factor in transcription-coupled DNA repair
Correction: Comparative analysis of chloroplast genomes of seven Juniperus species from Kazakhstan
Reply to van Schie et al.: ARK2N in TCR: Across in vivo and in vitro studies
Translation and cultural adaption of MacLeod Clark professional identity scale among Chinese therapy students
Background Fostering a strong professional identity (PI) enhances career fulfillment. In China, therapy education is undergoing development, integrating both Western and traditional health concepts, causing inconsistent PI among therapy students. To date, no validated tools exist to measure and monitor PI of Chinese therapy students. This study aimed to translate and validate the 9-item MacLeod Clark Professional Identity Scale (MCPIS-9) for this purpose. Design This study involved translation and cultural adaptation of the MCPIS-9, followed by a rigorous assessment of its model fit and psychometric properties using data collected via an online questionnaire. Methods A forward- and backward- translation process was conducted. Content validity was evaluated using item-level content validity index (I-CVI) and scale level content validity index average method (S-CVI/Ave). Therapy students across all grades at undergraduate and postgraduate levels in China were eligible. Exploratory factor analysis (EFA) examined the underlying factor structure. Model fit was evaluated through confirmatory factor analysis (CFA) using the Comparative Fit Index (CFI), Tucker-Lewis Index (TLI), Standardized Root Mean Square Residual (SRMR) and Root Mean Square of Error of Approximation (RMSEA). Convergent validity was assessed through Pearson’s correlations coefficient (r) with the Professional Identity Scale for Health Students and Professionals (PISHSP). Internal consistency was examined using Cronbach’s Alpha (Cα) and McDonald’s Omega (ω). Results A total of 1054 students participated. Content validity was excellent (I-CVI = 0.86–1.0, S-CVI/Ave = 0.98). EFA indicated a two-factor structure with acceptable model fit (CFI = 0.978; TLI = 0.968; SRMR = 0.033; RMSEA = 0.063). Reliability was strong (Cα = 0.835; ω = 0.817). Convergent validity demonstrated a strong correlation (r = 0.75) with the PISHSP. Conclusions The Chinese MCPIS-9 is a reliable and valid tool for assessing PI among therapy students. Future research could focus on refining item 4 of this tool, potentially through further exploration of therapy students’ perceptions of PI within the unique context of the Chinese healthcare system.
Macromolecular interactions and geometrical confinement determine the 3D diffusion of ribosome-sized particles in live <i>Escherichia coli</i> cells
The crowded bacterial cytoplasm is composed of biomolecules that span several orders of magnitude in size and electrical charge. This complexity has been proposed as the source of the rich spatial organization and apparent anomalous diffusion of intracellular components, although this has not been tested directly. Here, we use biplane microscopy to track the 3D motion of self-assembled bacterial genetically encoded multimeric nanoparticles (bGEMs) with tunable size (20 to 50 nm) and charge (−3,240 to +2,700 e) in live Escherichia coli cells. To probe intermolecular details at spatial and temporal resolutions beyond experimental limits, we also developed a colloidal whole-cell model that explicitly represents the size and charge of cytoplasmic macromolecules and the porous structure of the bacterial nucleoid. Combining these techniques, we show that bGEMs spatially segregate by size, with small 20-nm particles enriched inside the nucleoid, and larger and/or positively charged particles excluded from this region. Localization is driven by entropic and electrostatic forces arising from cytoplasmic polydispersity, nucleoid structure, geometrical confinement, and interactions with other biomolecules including ribosomes and DNA. We observe that at the timescales of traditional single molecule tracking experiments, motion appears subdiffusive for all particle sizes and charges. However, using computer simulations with higher temporal resolution, we find that the apparent anomalous exponents are governed by the region of the cell in which bGEMs are located. Molecular motion does not display anomalous diffusion on short time scales and the apparent subdiffusion arises from geometrical confinement within the nucleoid and by the cell boundary.
Impact of antibiotics, iron oxide, and sodium sulfate on microbial community composition in laboratory-built municipal solid waste microcosms
Municipal solid waste (MSW) landfills represent underexplored microbial ecosystems. Landfills contain variable amounts of antibiotic and construction and demolition (C&D) wastes, which have the potential to alter microbial metabolism due to biocidal or redox active components, and these effects are largely underexplored. To circumvent the challenge of MSW heterogeneity, we conducted a 65-day time series study on simulated MSW microcosms to assess microbiome changes using 16S rRNA sequencing in response to 1) Fe(OH)3 and 2) Na2SO4 to represent redox active components of C&D waste as well as 3) antibiotics. The addition of Fe(OH)3 altered the overall community composition and increased Shannon diversity and Chao1 richness. The addition of a mixture of seven antibiotics (1000 ng/L each) altered the community composition without affecting diversity metrics. Sulfate addition had little effect on microbial community composition or diversity. These results suggest that the microbial community composition in fresh MSW may be significantly impacted by influxes of iron waste and a single application of antibiotics.
Automating alloy design and discovery with physics-aware multimodal multiagent AI
The design of new alloys is a multiscale problem that requires a holistic approach that involves retrieving relevant knowledge, applying advanced computational methods, conducting experimental validations, and analyzing the results, a process that is typically slow and reserved for human experts. Machine learning can help accelerate this process, for instance, through the use of deep surrogate models that connect structural and chemical features to material properties, or vice versa. However, existing data-driven models often target specific material objectives, offering limited flexibility to integrate out-of-domain knowledge and cannot adapt to new, unforeseen challenges. Here, we overcome these limitations by leveraging the distinct capabilities of multiple AI agents that collaborate autonomously within a dynamic environment to solve complex materials design tasks. The proposed physics-aware generative AI platform, AtomAgents, synergizes the intelligence of large language models (LLMs) and the dynamic collaboration among AI agents with expertise in various domains, including knowledge retrieval, multimodal data integration, physics-based simulations, and comprehensive results analysis across modalities. The concerted effort of the multiagent system allows for addressing complex materials design problems, as demonstrated by examples that include autonomously designing metallic alloys with enhanced properties compared to their pure counterparts. Our results enable accurate prediction of key characteristics across alloys and highlight the crucial role of solid solution alloying to steer the development of advanced metallic alloys. Our framework enhances the efficiency of complex multiobjective design tasks and opens avenues in fields such as biomedical materials engineering, renewable energy, and environmental sustainability.
Climate change heterogeneity: A new quantitative approach
Climate change is a spatial and temporarily non-uniform phenomenon that requires understanding its evolution to better evaluate its potential societal and economic impact. The value added of this paper lies in introducing a quantitative methodology grounded in the trend analysis of temperature distribution quantiles to analyze climate change heterogeneity (CCH). By converting these quantiles into time series objects, the methodology empowers the definition and measurement of various relevant concepts in climate change analysis (warming, warming typology, warming amplification and warming acceleration) in a straightforward and robust testable linear regression format. It also facilitates the introduction of new testable concepts like warming dominance to compare (globally or partially) the warming process experienced by different regions. Furthermore, the methodology holds the added significance of concurrently encompassing both temporal and spatial dimensions in temperature analysis, owing to the close alignment between unconditional quantiles and latitude measures. Applying our quantitative methodology for the period 1950-2019 to the Globe (2192 stations) and Spain (30 stations) as a benchmark region, we find that both experience a distributional warming process (beyond the standard average) but of very different types. While the Globe experiences a stronger warming in the lower temperatures than in the upper ones, Spain evolves from equal warming in the whole distribution toward a stronger warming in the upper quantiles (similar to the warming process experienced in the African continent). In the two cases, the warming process accelerates (non-linear behavior) over time and is asymmetrically amplified. Overall, although both the Globe and Spain suffer an equivalent warming process in the median (mean) temperature, Spain’s warming dominates the Globe in the upper quantiles and is dominated in the lower tail of the global temperature distribution that corresponds to the Arctic region. Our climate change heterogeneity results open the door to the need for a non-uniform causal-effect climate analysis that goes beyond the standard causality in mean and for a more efficient design of the mitigation-adaptation policies. In particular, the heterogeneity found suggests these policies should contain a common global component and a clear local-regional idiosyncratic element. The latter is usually more straightforward to implement.
Characterizing temporal and global host innate immune responses against SARS-CoV-1 and -2 infection in pathologically relevant human lung epithelial cells
Severe acute respiratory syndrome coronavirus-1 (SARS-CoV-1) and -2 (SARS-CoV-2) are beta-coronaviruses (β-CoVs) that have caused significant morbidity and mortality worldwide. Therefore, a better understanding of host responses to β-CoVs would provide insights into the pathogenesis of these viruses to identify potential targets for medical countermeasures. In this study, our objective is to use a systems biology approach to explore the magnitude and scope of innate immune responses triggered by SARS-CoV-1 and -2 infection over time in pathologically relevant human lung epithelial cells (Calu-3/2B4 cells). Total RNA extracted at 12, 24, and 48 hours after β-CoVs or mock infection of Calu-3/2B4 cells were subjected to RNA sequencing and functional enrichment analysis to select genes whose expressions were significantly modulated post-infection. The results demonstrate that SARS-CoV-1 and -2 stimulate similar yet distinct innate antiviral signaling pathways in pathologically relevant human lung epithelial cells. Furthermore, we found that many genes related to the viral life cycle, interferons, and interferon-stimulated genes (ISGs) were upregulated at multiple time points. Based on their profound modulation upon infection by SARS-CoV-1, SARS-CoV-2, and Omicron BA.1, four ISGs, i.e., bone marrow stromal cell antigen 2 (BST2), Z-DNA Binding Protein 1 (ZBP1), C-X-C Motif Chemokine Ligand 11 (CXCL11), and Interferon Induced Transmembrane Protein 1 (IFITM1), were identified as potential drug targets against β-CoVs. Our findings suggest that these genes affect both pathogens directly and indirectly through the innate immune response, making them potential targets for host-directed antivirals. Altogether, our results demonstrate that SARS-CoV-1 and SARS-CoV-2 infection induce differential effects on host innate immune responses.
3D ophthalmic ultrasonography at the slit lamp using existing ultrasound systems
Purpose This study aims to explore the feasibility and performance of three-dimensional ultrasound (3DUS) imaging in ophthalmology using commercially available ultrasound probes adapted to a slit lamp. Significance Despite ultrasound’s long-standing application in eye care for visualizing ocular components, the evolution of 3DUS technology has remained inactive, with limited development and commercial availability. This study introduces a novel method that could potentially enhance ophthalmic diagnostics and treatment planning by providing comprehensive 3D views of ocular structures using existing ultrasound probes adapted to the conventional slit lamp. Methods A custom system was designed for mounting a linear actuator to a slit lamp and enabling the horizontal actuation of any ultrasound probe. Ophthalmic and non-ophthalmic ultrasound probes were tested for their ability to reconstruct 3DUS images of the posterior pole. The study involved designing and evaluating three US phantoms ex vivo and performing in vivo imaging on human subjects to assess the system’s applicability. Results The system successfully acquired 3D volume scans of phantoms and live human eyes, demonstrating the system’s potential for detailed ocular imaging. The adaptability of the device allowed for compatibility with various ultrasound probes. In vivo imaging revealed the system’s capability to produce high-resolution 3D reconstructions of ocular structures, including eyes with pathological conditions. Conclusions The introduction of a slit lamp-mounted 3DUS system represents a significant advancement in ophthalmic ultrasonography, offering a practical and accessible solution for comprehensive 3D ocular assessments. The device’s modularity and compatibility with existing ultrasound probes make it a versatile tool for a wide range of ophthalmic applications. Further research and clinical trials are needed to optimize the system’s performance and validate its clinical utility.
Variants in the SOX9 transactivation middle domain induce axial skeleton dysplasia and scoliosis
SOX9 is a crucial transcriptional regulator of cartilage development and homeostasis. Dysregulation of SOX9 is associated with a wide spectrum of skeletal disorders, including campomelic dysplasia, acampomelic campomelic dysplasia, and scoliosis. Yet how SOX9 variants contribute to the spectrum of axial skeletal disorders is not well understood. Here, we report four pathogenic variants of SOX9 identified in a cohort of patients with congenital vertebral malformations. We report a pathogenic missense variant in the transactivation middle (TAM) domain of SOX9 associated with mild skeletal dysplasia and scoliosis. We isolated a Sox9 mutant mouse with an in-frame microdeletion in the TAM domain ( Sox9 Asp272del ), which exhibits skeletal dysplasia including kinked tails, rib cage anomalies, and scoliosis in homozygous mutants. We find that both the human missense and the mouse microdeletion mutations resulted in reduced SOX9 protein stability in cell culture, while Sox9 Asp272del mutant mice show decreased SOX9 expression in the growth plate and annulus fibrosus tissues of the spine. This reduction in SOX9 expression was correlated with the reduction of extracellular matrix components, such as tenascin-X and the Adhesion G-protein coupled receptor ADGRG6. In summary, our work identified and modeled a pathologic variant of SOX9 within the TAM domain and demonstrated its importance for SOX9 protein stability. Our work demonstrates that SOX9 stability is important for the regulation of ADGRG6 expression, which is a known regulator of postnatal spine homeostasis, underscoring the essential role of SOX9 dosage in a spectrum of axial skeleton dysplasia in humans.
Impact of exercise with blood flow restriction on muscle hypertrophy and performance outcomes in men and women
Blood flow restriction training (BFRT) has been previously studied as an alternative form of resistance training to gain lean mass and improve performance outcomes. However, in all exercise studies of BFRT, the proportion of female participants represents only 17–29% of all research participants. This highlights a strong underrepresentation of females and the need for more knowledge on the impact of BFRT and sex differences. The primary objective was to compare the impact of 6-week BFRT on lean mass, strength, and performance outcomes between males and females. A total of 38 adults [age, 25.3 ± 3.1 years; female, n = 19 (50%)] performed whole-body resistance training program with blood flow restriction three times per week. Exercises were performed at 30% of 1-repetition maximum (1-RM) and blood flow restriction cuffs were set to 60% of each individual’s limb occlusion pressure. Body composition was assessed via dual-energy x-ray absorptiometry and strength was measured using 1-RM. A significant increase in lean mass was observed in males (p = 0.009) and females (p = 0.023) with no difference in the change between groups (p = 0.279). Both males and females increased 1-RM for upper- and lower-body exercises, with significant interaction effects (time x sex) for chest press (p = 0.003), seated row (p = 0.038), knee flexion (p = 0.043), and knee extension (p = 0.035), suggesting males increased 1-RM more for these exercises. Furthermore, peak power was improved in males (p < 0.001) and females (p = 0.002) during a vertical squat jump, but a significant interaction (time x sex) effect was observed (p = 0.039), suggesting males increased to a greater extent. Males and females significantly increased lean body mass, to a similar degree, following six weeks of resistance training in combination with blood flow restriction. Likewise, both males and females improved muscle strength following 6-week BFRT, however males may improve strength to a greater extent than females.
Diffusive topology preserving manifold distances for single-cell data analysis
Manifold learning techniques have emerged as crucial tools for uncovering latent patterns in high-dimensional single-cell data. However, most existing dimensionality reduction methods primarily rely on 2D visualization, which can distort true data relationships and fail to extract reliable biological information. Here, we present DTNE (diffusive topology neighbor embedding), a dimensionality reduction framework that faithfully approximates manifold distance to enhance cellular relationships and dynamics. DTNE constructs a manifold distance matrix using a modified personalized PageRank algorithm, thereby preserving topological structure while enabling diverse single-cell analyses. This approach facilitates distribution-based cellular relationship analysis, pseudotime inference, and clustering within a unified framework. Extensive benchmarking against mainstream algorithms on diverse datasets demonstrates DTNE’s superior performance in maintaining geodesic distances and revealing significant biological patterns. Our results establish DTNE as a powerful tool for high-dimensional data analysis in uncovering meaningful biological insights.
Impact of media coverage on the transmission dynamics of TB with vaccines and treatment
Tuberculosis (TB) is one of the deadly infectious diseases affecting millions of individuals throughout the world. The main objective of this study is to investigate the impact of media coverage on the transmission dynamics of TB with vaccine and treatment strategy using mathematical model analysis. In the qualitative analysis of the proposed model we proved the existence, uniqueness, positivity, and boundedness of the model solutions, investigated both the disease-free and endemic equilibrium points, computed the basic and effective reproduction numbers using next generation matrix approach, analyzed the stability analysis of the equilibrium points, the backward bifurcation using the Castillo-Chavez and Song theorem and we re-formulated the corresponding optimal control problem and analyzed by applying the Pontryagin’s Minimum Principle. In the model quantitative (numerical) analysis part, we performed the model parameters sensitivity analysis and carried out numerical simulation to verify the qualitative analysis results. The findings of the study indicate that if the reproduction number is less than one, the solution converges to the disease-free state, signifying the asymptotic stability of the TB-free steady state. Moreover, the existence of a backward bifurcation shows that the disease-free equilibrium coexists with one or more endemic equilibria, even when the basic reproduction number is less than 1. Furthermore, it is found that as media efficacy increases, the disease infection rate decreases, which consequently leads to an increase in prevention and treatment control strategies.
Representational geometry explains puzzling error distributions in behavioral tasks
Measuring and interpreting errors in behavioral tasks is critical for understanding cognition. Conventional wisdom assumes that encoding/decoding errors for continuous variables in behavioral tasks should naturally have Gaussian distributions, so that deviations from normality in the empirical data indicate the presence of more complex sources of noise. This line of reasoning has been central for prior research on working memory. Here, we reassess this assumption and find that even in ideal observer models with Gaussian encoding noise, the error distribution is generally non-Gaussian, contrary to the commonly held belief. Critically, we find that the shape of the error distribution is determined by the geometrical structure of the encoding manifold via a simple rule. In the case of a high-dimensional geometry, the error distributions naturally exhibit flat tails. Using this insight, we apply our theory to visual short-term memory tasks, and find that it can account for a large array of experimental data with only two free parameters. Our results challenge the dominant view in the mechanisms and capacity constraints of working memory systems. They instead suggest that the Bayesian framework, which explains various aspects of perceptual behavior, also provides an excellent account of working memory. Overall, our results establish a direct connection between neural manifold geometry and behavior, and call attention to the geometry of the representation as a critically important, yet underappreciated factor in determining the character of errors in human behavior.
The importance of method selection when estimating diet composition with quantitative fatty acid signature analysis
Quantitative fatty acid signature analysis (QFASA) is a common method of estimating the composition of prey species in the diets of consumers from polar and temperate ecosystems in which lipids are an important source of energy. A key characteristic of QFASA is that the large number of fatty acids that typically comprise lipids permits the dietary contributions of a correspondingly large number of prey types to be estimated. Several modifications to the original QFASA methods have been suggested in the literature and a significant extension of the original model published in 2017 allows simultaneous estimation of both diet proportions and calibration coefficients, which are metabolic constants in the model whose values must otherwise be estimated in independent feeding experiments. However, comparisons of diet estimates obtained using different estimation options have been limited. QFASA has been used to estimate the diet composition of several polar bear (Ursus maritimus) subpopulations, including the Southern Beaufort Sea (SBS) subpopulation. Prior QFASA estimates of SBS polar bear diet composition have most often been obtained using variations of the original QFASA model. We investigated the influence of variations in QFASA analytical methods on diet estimates by re-estimating the diet composition of polar bears from the Alaska portion of the SBS using three different methods and found that differences among the three sets of estimates were substantial. Our results illustrate how important the careful and deliberate selection of QFASA methods can be and we provide some guidance on techniques one might use to evaluate options.
The <i>Pseudomonas</i> ligninolytic catalytic network reveals the importance of auxiliary enzymes in lignin biocatalysts
Lignin degradation by biocatalysts is a key strategy to develop a plant-based sustainable carbon economy and thus alleviate global climate change. This process involves synergy between ligninases and auxiliary enzymes. However, auxiliary enzymes within secretomes, which are composed of thousands of enzymes, remain enigmatic, although several ligninolytic enzymes have been well characterized. Moreover, it is a challenge to understand synergistic lignin degradation via a diverse array of enzymes, especially in bacterial systems. In this study, the coexpression network of the periplasmic proteome uncovers potential accessory enzymes for B-type dye-decolorizing peroxidases (DypBs) in Pseudomonas putida A514. The catalytic network of the DypBs-based multienzyme complex is characterized. DypBs couple with quinone reductases and nitroreductase to participate in quinone redox cycling. They work with superoxide dismutase to induce Fenton reaction for lignin oxidation. A synthetic enzyme cocktail (SEC), recruiting 15 enzymes, was consequently designed with four functions. It overcomes the limitation of lignin repolymerization, exhibiting a capacity comparable to that of the native periplasmic secretome. Importantly, we reveal the synergistic mechanism of a SEC-A514 cell system, which incorporates the advantages of in vitro enzyme catalysis and in vivo microbial catabolism. Chemical analysis shows that this system significantly reduces the molecular weight of lignin, substantially extends the degradation spectra for lignin functional groups, and efficiently metabolizes lignin derivatives. As a result, 25% of lignin is utilized, and its average molecular weight is reduced by 27%. Our study advances the knowledge of bacterial lignin-degrading multienzymes and provides a viable lignin degradation strategy.